AIMC Topic: Machine Learning

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Classification Criteria for Cytomegalovirus Retinitis.

American journal of ophthalmology
PURPOSE: The purpose of this study was to determine classification criteria for cytomegalovirus (CMV) retinitis.

Classification Criteria for Behçet Disease Uveitis.

American journal of ophthalmology
PURPOSE: To determine classification criteria for Behçet disease uveitis.

Assistance from Automated ASPECTS Software Improves Reader Performance.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
PURPOSE: To compare physicians' ability to read Alberta Stroke Program Early CT Score (ASPECTS) in patients with a large vessel occlusion within 6 hours of symptom onset when assisted by a machine learning-based automatic software tool, compared with...

Rise of Clinical Studies in the Field of Machine Learning: A Review of Data Registered in ClinicalTrials.gov.

International journal of environmental research and public health
Although advances in machine-learning healthcare applications promise great potential for innovative medical care, few data are available on the translational status of these new technologies. We aimed to provide a comprehensive characterization of t...

Cross-Predicting Essential Genes between Two Model Eukaryotic Species Using Machine Learning.

International journal of molecular sciences
Experimental studies of and have contributed substantially to our understanding of molecular and cellular processes in metazoans at large. Since the publication of their genomes, functional genomic investigations have identified genes that are esse...

Two-step machine learning method for the rapid analysis of microvascular flow in intravital video microscopy.

Scientific reports
Microvascular blood flow is crucial for tissue and organ function and is often severely affected by diseases. Therefore, investigating the microvasculature under different pathological circumstances is essential to understand the role of the microcir...

Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance.

Nature communications
Resistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learnin...

A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy.

Nature communications
Transthyretin amyloid cardiomyopathy, an often unrecognized cause of heart failure, is now treatable with a transthyretin stabilizer. It is therefore important to identify at-risk patients who can undergo targeted testing for earlier diagnosis and tr...